Integrative approaches for large-scale transcriptome-wide association studies

Abstract

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. In this work we introduce a powerful strategy that integrates gene expression measurements with large-scale genome-wide association data to identify genes whose cis-regulated expression is associated to complex traits. We use a relatively small reference panel of individuals for which both genetic variation and gene expression have been measured to impute gene expression into large cohorts of individuals and identify expression-trait associations. We extend our methods to allow for indirect imputation of the expression-trait association from summary association statistics of large-scale GWAS 1-3 . We applied our approaches to expression data from blood and adipose tissue measured in ~3,000 individuals overall. We then imputed gene expression into GWAS data from over 900,000 phenotype measurements 4-6 to identify 69 novel genes significantly associated to obesity-related traits (BMI, lipids, and height). Many of the novel genes were associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Overall our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression

“…Therefore, it does not prove causality, similarly to all current methods. [7][8][9][10][11][12] In fact, if gene functions are often pleiotropic, for which we found some evidence in the current study, the assumption of independence between the instrument variable and the outcome of Mendelian randomization approaches is more likely to be violated. 34 Still, the inferences made here can potentially guide phenotypic studies of gene functions in model systems.…”

Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

“…43 Unlike GWASs, where the association between a genetic variant and trait is unidirectional, in transcriptome-wide association studies (TWASs) the direction of association between transcript and phenotype is not clear and causal inference must be drawn with caution. 44 eQTL studies hold the promise of revealing biological mechanisms of SNP-phenotype associations; integrating GWASs with TWASs may help prioritize genes and variants for functional studies. 44 In this study, we used a causal inference approach to infer causal relations and their directionality by integrating SNPs from GWASs with gene expression and phenotype data predicated on the assumption that if a gene is causally related to a phenotype, a nearby genetic variant (i.e., a cis-eQTL) that explains a large proportion of its expression should be associated with the same phenotype.…”

10 Years of GWAS Discovery: Biology, Function, and Translation

“…One fertile area of method development is integrating data from GWASs and expression quantitative trait locus (eQTL) studies to identify associations between transcripts and complex traits. 56,61,62 These methods are useful for prioritizing genes from known GWAS loci for functional follow-up, detecting novel gene-trait associations, and inferring the directions of associations. 21,27,62 The analytical results that only about one-third of the associated genes are the nearest genes 61,62 are informative for the design of fine-mapping experiments.…”